Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

Bayes Estimators for Phylogenetic Reconstruction

P. M. Huggins, W. Li, D. Haws, T. Friedrich, J. Liu and R. Yoshida
Systematic Biology
Vol. 60, No. 4 (JULY 2011), pp. 528-540
Stable URL: http://www.jstor.org/stable/41316553
Page Count: 13
  • Download ($42.00)
  • Cite this Item
Bayes Estimators for Phylogenetic Reconstruction
Preview not available

Abstract

Tree reconstruction methods are often judged by their accuracy, measured by how close they get to the true tree. Yet, most reconstruction methods like maximum likelihood (ML) do not explicitly maximize this accuracy. To address this problem, we propose a Bayesian solution. Given tree samples, we propose finding the tree estimate that is closest on average to the samples. This "median" tree is known as the Bayes estimator (BE). The BE literally maximizes posterior expected accuracy, measured in terms of closeness (distance) to the true tree. We discuss a unified framework of BE trees, focusing especially on tree distances that are expressible as squared euclidean distances. Notable examples include Robinson-Foulds (RF) distance, quartet distance, and squared path difference. Using both simulated and real data, we show that BEs can be estimated in practice by hill-climbing. In our simulation, we find that BEs tend to be closer to the true tree, compared with ML and neighbor joining. In particular, the BE under squared path difference tends to perform well in terms of both path difference and RF distances.

Page Thumbnails

  • Thumbnail: Page 
528
    528
  • Thumbnail: Page 
529
    529
  • Thumbnail: Page 
530
    530
  • Thumbnail: Page 
531
    531
  • Thumbnail: Page 
532
    532
  • Thumbnail: Page 
533
    533
  • Thumbnail: Page 
534
    534
  • Thumbnail: Page 
535
    535
  • Thumbnail: Page 
536
    536
  • Thumbnail: Page 
537
    537
  • Thumbnail: Page 
538
    538
  • Thumbnail: Page 
539
    539
  • Thumbnail: Page 
540
    540